An Intelligent Approach to Handle False-Positive Radio Frequency Identification Anomalies

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Title An Intelligent Approach to Handle False-Positive Radio Frequency Identification Anomalies
Author Darcy, Peter John; Stantic, Bela; Sattar, Abdul
Journal Name Intelligent Data Analysis Journal
Year Published 2011
Place of publication Netherlands
Publisher IOS Press
Abstract Radio Frequency Identification (RFID) technology allows wireless interaction between tagged objects and readers to automatically identify large groups of items. This technology is widely accepted in a number of application domains, however, it suffers from data anomalies such as false-positive observations. Existing methods, such as manual tools, user specified rules and filtering algorithms, lack the automation and intelligence to effectively remove ambiguous false-positive readings. In this paper, we propose a methodology which incorporates a highly intelligent feature set definition utilised in conjunction with various state-of-the-art classifying techniques to correctly determine if a reading flagged as a potential false-positive anomaly should be discarded. Through experimental study we have shown that our approach cleans highly ambiguous false-positive observational data effectively. We have also discovered that the Non-Monotonic Reasoning classifier obtained the highest cleaning rate when handling false-positive RFID readings.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.3233/IDA-2011-0503
Copyright Statement Copyright 2011 IOS Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal website for access to the definitive, published version.
Volume 15
Issue Number 6
Page from 931
Page to 954
ISSN 1088-467X
Date Accessioned 2012-02-27; 2012-04-01T23:04:50Z
Date Available 2012-04-01T23:04:50Z
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Artificial Intelligence and Image Processing; Data Format
URI http://hdl.handle.net/10072/44149
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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